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Raptor-ai: An open-source AI powered radiation protection toolkit for radioisotopes

  • Hiroshi Watabe
  • , Peter K.N. Yu
  • , Dragana Krstic
  • , Dragoslav Nikezic
  • , Kyeong Min Kim
  • , Taiga Yamaya
  • , Naoki Kawachi
  • , Hiroki Tanaka
  • , Zoran Jovanovic
  • , A. K.F. Haque
  • , M. Rafiqul Islam
  • , Gary Tse
  • , Quinncy Lee
  • , Mehrdad Shahmohammadi Beni

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

Abstract

Artificial intelligence (AI) has gained significant attention in various scientific fields due to its ability to process large datasets. In nuclear radiation physics, while AI presents exciting opportunities, it cannot replace physics-based models essential for explaining radiation interactions with matter. To combine the strengths of both, we have developed and open-sourced the Radiation Protection Toolkit for Radioisotopes with Artificial Intelligence (RAPTOR-AI). This toolkit integrates AI with the Particle and Heavy Ion Transport code System (PHITS) Monte Carlo package, enabling rapid radiation protection analysis for radioisotopes and structural shielding. RAPTOR-AI is particularly valuable for emergency scenarios, allowing quick dose dispersion assessments when a facility's structural map is available, enhancing safety and response efficiency.

Original languageEnglish
Article number111797
Number of pages9
JournalApplied Radiation and Isotopes
Volume221
DOIs
Publication statusPublished - Jul 2025

Keywords

  • Artificial intelligence
  • Monte Carlo method
  • Nuclear radiation
  • Open-source
  • Radiation protection

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